Agenda Day 1
7:30 am - 8:30 am
Networking Breakfast & Registration
8:30 am - 8:40 am
Chairperson's Opening Address
8:40 am - 9:10 am
KEYNOTE PANEL DISCUSSION: Realise the Ambition of Fully Digitalised R&D Through Connected Data, Knowledge and Governance
Roman Affentranger -
Executive Director, Global Head of Software & Workflows, BR Data & Digital,
Novartis
Tatjana Sentjabrskaja - Head of R&D Digitalization & Automation, Henkel
Niels Vandervoort - Director Digital Transformation Synthetics Development, Johnson & Johnson Innovative Medicine
Leticia Espinosa Alonso - Head of Digital, Food & Beverages Biosolutions Applied R&D, Novonesis
George Koutitas - Senior Director, R&D AI Technical Product Lead, Forward Deployed Technology, Pfizer
Fragmented data, disconnected systems and growing volumes of
multimodal information continue to limit the ability to scale innovation
across R&D. Creating the foundations needed to connect information,
generate reusable knowledge and support enterprise-wide decision-making
remains a critical challenge.
• What governance, metadata and ownership structures are needed to
maximise the value of R&D data through enterprise data products?
• Where can interoperable architectures improve enterprise-wide data
accessibility, reuse and traceability by connecting systems and scientific
workflows?
• To what extent can a unified knowledge layer spanning legacy and newly
generated data accelerate discovery, decision-making and AI adoption?
Roman Affentranger
Executive Director, Global Head of Software & Workflows, BR Data & DigitalNovartis
Niels Vandervoort
Director Digital Transformation Synthetics DevelopmentJohnson & Johnson Innovative Medicine
Leticia Espinosa Alonso
Head of Digital, Food & Beverages Biosolutions Applied R&DNovonesis
George Koutitas
Senior Director, R&D AI Technical Product Lead, Forward Deployed TechnologyPfizer
9:10 am - 9:40 am
Can We Build Dark Labs with Dark Data? Shedding Light on the Foundations of AI-Driven Experimentation
Anna Codina -
Senior Director Strategy & Business Development,
SciY
Scientific data remains inaccessible when trapped within instruments,
proprietary formats and isolated applications. Without consistent
connectivity, context and governance at the point of generation, laboratories
cannot reliably apply AI or extend scientific information across the product
lifecycle.
• Close the gap between AI ambition and laboratory reality by resolving
fragmented, inaccessible and insufficiently contextualised scientific data
• Accelerate instrument onboarding and data accessibility by eliminating
dark data across new and legacy laboratory technologies at scale
• Unlock intelligent workflow orchestration and AI-driven experimentation
through connected, contextualised and reusable scientific data
Sponsored by: SciY
Sponsored by: SciY
10:55 am - 11:25 am
One-To-One Business Meetings & Networking
11:30 am - 12:00 pm
Redefine Data Governance to Accelerate Digital Adoption
Data governance has become essential for creating trusted and reusable
data. However, many governance programmes struggle because they
prioritize control over usability.
• Accelerate AI readiness by transforming data into trusted enterprise assets
through governance frameworks that improve quality, ownership and reusability
• Deliver enterprise-wide adoption by embedding governance into scientific
workflows without compromising researcher productivity and flexibility
• Secure long-term value from digital transformation by aligning standards,
accountability and user experience across the organisation
12:00 pm - 12:30 pm
Turn AI Ambition into Deployment-Ready Scientific Use Cases
Pressure to apply AI is growing, yet scientific data is rarely ready for
immediate consumption by AI models. Instrument outputs must be
connected with methods, metadata and operational context, while regulated
environments introduce additional validation requirements that can prevent
promising use cases from progressing beyond experimentation.
• De-risk AI investment by assessing digital debt, data quality, scientific
context and system dependencies before committing to a use case
• Maximise AI value by directing resources towards use cases with the
strongest combination of business impact, data readiness and scalability
• Accelerate deployment by contextualising scientific data and sequencing
the technology, data and process improvements required for a validated
proof of concept
Sponsored by: Astrix
12:35 pm - 1:05 pm
One-To-One Business Meetings & Networking
1:05 pm - 2:05 pm
Networking Lunch
Data generated throughout the product lifecycle is often confined to
individual functions, limiting visibility and preventing organisations from
extracting its full value. Breaking down these silos is becoming increasingly
important to support enterprise-wide decision-making and innovation.
• Enhance the value of R&D data by extending its impact beyond the
laboratory to support manufacturing, quality and business decision-making
• Accelerate innovation and operational performance by enabling the
continuous exchange of insights between R&D, manufacturing and quality
functions
• Drive enterprise-wide visibility and AI readiness by establishing a
connected data continuum that preserves context, traceability and
meaning across the product lifecycle
Gautam Mishra
Head of Corporate Research Analytical Lab, EMEA & Global Weathering Research Cen3M
2:40 pm - 3:10 pm
PANEL DISCUSSION: Win Buy-In from R&D Transformation Leaders & Researchers with Clear Business Cases for Sustained Technology Adoption
Martin Schraub -
Director - Program Lead Digital R&D,
Merck KGaA, Darmstadt
Vera Hazelwood - Director of Enterprise AI Capability & Engagement, Enterprise AI Transformation, AstraZeneca UK
Claudio Battilocchio - Global Head Digital Automation & Process Innovation R&D, Syngenta Crop Protection
Ilaria Ferlenghi - Associate Director, Head of Structural Microscopy, GSK
Technology implementation often stalls when researchers, functional
stakeholders and R&D leaders lack a shared understanding of its value,
or when evidence takes too long to emerge. As technology advances
and investment decisions become more time-sensitive, R&D teams need
to establish a clear value case, validate it with evidence and translate
productivity gains into meaningful outcomes.
• Build a value hypothesis before investing by defining the R&D challenge,
expected impact and value of the investment, not just its potential financial
return
• Secure active stakeholder commitment to strategy execution by linking
technology initiatives to measurable R&D outcomes, strategic priorities and
tangible business value, with shared measures of success and ownership
across functions
• Establish researcher trust and sustain participation by demonstrating how
individual adoption contributes to scientific outcomes and wider business
impact throughout implementation
Vera Hazelwood
Director of Enterprise AI Capability & Engagement, Enterprise AI TransformationAstraZeneca UK
Claudio Battilocchio
Global Head Digital Automation & Process Innovation R&DSyngenta Crop Protection
3:10 pm - 3:40 pm
Minimise Experimental Testing Burden & Accelerate DMTA Cycles Through AI
Increasing pressure to accelerate discovery is driving organisations to
identify ways of reducing experimental burden without compromising
scientific confidence. Advances in AI-driven prediction, simulation and
in silico experimentation are enabling researchers to prioritise the most
promising candidates earlier, reducing the number of physical experiments
required to progress through discovery and development.
• Expedite quality lead discovery by using AI-driven prediction and virtual
experimentation to focus resources on the most promising compounds and
targets
• Minimise experimental burden through AI-driven experiment design that maximises
data and insight generation while minimising unnecessary wet-lab testing
• Improve lead progression decisions through predictive AI and scientific
expertise that increase confidence in candidate readiness
Sponsored by: Albert Invent
Select pin badges that reflect your professional focus areas and personal
interests. Discover who is in the room, benchmark challenges with peers
facing similar priorities, and connect with adjacent functions to gain fresh
perspectives on the realities of leading AI, data and digital transformation
across R&D.
• Build meaningful connections with peers who share similar R&D, digital and
transformation priorities
• Benchmark challenges, priorities and approaches with leaders from both
core and adjacent functions
4:20 pm - 4:50 pm
One-To-One Business Meetings & Networking
4:55 pm - 5:25 pm
Succeed Beyond Pilot by Demonstrating Meaningful ROI from Digital Transformation Beyond Traditional Metrics
Sponsored by: Albert Invent
